To address this challenge, we resort to data augmentation methods. Data augmentation is commonly utilized in deep learning, e.g., for image classification tasks, where the agent is made to learn becoming invariant to modifications of the data, e.g., image rotation, clipping, etc., by providing the agent with such modified inputs during the training [1]}. In our case, we apply data augmentation by randomly shuffling the order of resources first in time and then in frequency. The rows of \(S_t\)  and the resource selection probabilities at the output layer of the DNN follow this order. To illustrate our method, consider the example in Fig. REF . A raster-scan ordering of the resources in the resource pool is \({[r_1,r_2,r_3,r_4]}\) . We first group the resources sharing each subframe (corresponding to “columns” of the resource pool), and randomize the order of these groups. This yields a raster-scan ordering of, e.g., \({[r_2,r_1,r_4,r_3]}\) . Then, we group the resources sharing each subchannel, i.e., the “rows” of the pool, and randomly shuffle the order of the “rows”. This way, the convolutional network becomes invariant to the order of resources in time or frequency, while being able to infer the HD constraints among the resources.
